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- All HBS Web
(2,936)
- News (467)
- Research (2,185)
- Events (43)
- Multimedia (14)
- Faculty Publications (1,410)
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- 2009
- Case
What People Want (and How to Predict It)
By: Thomas H. Davenport and Jeanne G. Harris
Historically, neither the creators nor the distributors of cultural products such as books or movies have used analytics -- data, statistics, predictive modeling -- to determine the likely success of their offerings. Instead, companies relied on the brilliance of... View Details
Keywords: Product Development; Creativity; Customer Satisfaction; Forecasting and Prediction; Markets; Business Model; Publishing Industry; Motion Pictures and Video Industry
Davenport, Thomas H., and Jeanne G. Harris. "What People Want (and How to Predict It)." 2009.
- June 2021
- Article
From Predictions to Prescriptions: A Data-driven Response to COVID-19
By: Dimitris Bertsimas, Léonard Boussioux, Ryan Cory-Wright, Arthur Delarue, Vassilis Digalakis Jr, Alexander Jacquillat, Driss Lahlou Kitane, Galit Lukin, Michael Lingzhi Li, Luca Mingardi, Omid Nohadani, Agni Orfanoudaki, Theodore Papalexopoulos, Ivan Paskov, Jean Pauphilet, Omar Skali Lami, Bartolomeo Stellato, Hamza Tazi Bouardi, Kimberly Villalobos Carballo, Holly Wiberg and Cynthia Zeng
The COVID-19 pandemic has created unprecedented challenges worldwide. Strained healthcare providers make difficult decisions on patient triage, treatment and care management on a daily basis. Policy makers have imposed social distancing measures to slow the disease, at... View Details
Keywords: COVID-19; Health Pandemics; AI and Machine Learning; Forecasting and Prediction; Analytics and Data Science
Bertsimas, Dimitris, Léonard Boussioux, Ryan Cory-Wright, Arthur Delarue, Vassilis Digalakis Jr, Alexander Jacquillat, Driss Lahlou Kitane, Galit Lukin, Michael Lingzhi Li, Luca Mingardi, Omid Nohadani, Agni Orfanoudaki, Theodore Papalexopoulos, Ivan Paskov, Jean Pauphilet, Omar Skali Lami, Bartolomeo Stellato, Hamza Tazi Bouardi, Kimberly Villalobos Carballo, Holly Wiberg, and Cynthia Zeng. "From Predictions to Prescriptions: A Data-driven Response to COVID-19." Health Care Management Science 24, no. 2 (June 2021): 253–272.
- August 2020 (Revised September 2020)
- Technical Note
Assessing Prediction Accuracy of Machine Learning Models
The note introduces a variety of methods to assess the accuracy of machine learning prediction models. The note begins by briefly introducing machine learning, overfitting, training versus test datasets, and cross validation. The following accuracy metrics and tools... View Details
Keywords: Machine Learning; Statistics; Econometric Analyses; Experimental Methods; Data Analysis; Data Analytics; Forecasting and Prediction; Analytics and Data Science; Analysis; Mathematical Methods
Toffel, Michael W., Natalie Epstein, Kris Ferreira, and Yael Grushka-Cockayne. "Assessing Prediction Accuracy of Machine Learning Models." Harvard Business School Technical Note 621-045, August 2020. (Revised September 2020.)
- 09 Feb 2007
- Working Paper Summaries
Do Corporate Social Responsibility Ratings Predict Corporate Social Performance?
- August 2018 (Revised September 2018)
- Supplement
Predicting Purchasing Behavior at PriceMart (B)
By: Srikant M. Datar and Caitlin N. Bowler
Supplements the (A) case. In this case, Wehunt and Morse are concerned about the logistic regression model overfitting to the training data, so they explore two methods for reducing the sensitivity of the model to the data by regularizing the coefficients of the... View Details
Keywords: Data Science; Analytics and Data Science; Analysis; Customers; Household; Forecasting and Prediction
Datar, Srikant M., and Caitlin N. Bowler. "Predicting Purchasing Behavior at PriceMart (B)." Harvard Business School Supplement 119-026, August 2018. (Revised September 2018.)
- July 2006
- Article
Climate Change As a Predictable Surprise
By: M. H. Bazerman
Keywords: Change
Bazerman, M. H. "Climate Change As a Predictable Surprise." Climatic Change (July 2006): 1–15.
- 2019
- Working Paper
The Wisdom of Crowds in Operations: Forecasting Using Prediction Markets
By: Achal Bassamboo, Ruomeng Cui and Antonio Moreno
Prediction is an important activity in various business processes, but it becomes difficult when historical information is not available, such as forecasting demand of a new product. One approach that can be applied in such situations is to crowdsource opinions from... View Details
Keywords: Wisdom Of Crowds; Demand Forecasting; Price Forecasting; Forecasting and Prediction; Social and Collaborative Networks; Size; Performance
Bassamboo, Achal, Ruomeng Cui, and Antonio Moreno. "The Wisdom of Crowds in Operations: Forecasting Using Prediction Markets." Working Paper, 2019.
- October 17, 2022
- Article
Relational Diversity in Social Portfolios Predicts Well-Being
By: Hanne K. Collins, Serena F. Hagerty, Jordi Quoidbach, Michael I. Norton and Alison Wood Brooks
We document a link between the relational diversity of one’s social portfolio—the richness and evenness of relationship types across one’s social interactions—and well-being. Across four distinct samples, respondents from the United States who completed a preregistered... View Details
Keywords: Social Interaction; Social Engagement; Well-being; Happiness; Social and Collaborative Networks; Family and Family Relationships
Collins, Hanne K., Serena F. Hagerty, Jordi Quoidbach, Michael I. Norton, and Alison Wood Brooks. "Relational Diversity in Social Portfolios Predicts Well-Being." Proceedings of the National Academy of Sciences 119, no. 43 (October 17, 2022).
- Article
Shorting Demand and Predictability of Returns
By: Lauren Cohen, Karl B. Diether and Christopher J. Malloy
Cohen, Lauren, Karl B. Diether, and Christopher J. Malloy. "Shorting Demand and Predictability of Returns." Journal of Investment Management 7, no. 1 (First Quarter 2009): 36–52.
- April 2021 (Revised August 2021)
- Case
Borusan CAT: Monetizing Prediction in the Age of AI (A)
By: Navid Mojir and Gamze Yucaoglu
Borusan Cat is an international distributor of Caterpillar heavy machines. Esra Durgun (Director of Strategy, Digitization, and Innovation) and Ozgur Gunaydin (CEO) seem to have bet their careers on developing Muneccim, a new predictive technology that is designed to... View Details
Keywords: Monetization Strategy; Artificial Intelligence; AI; Forecasting and Prediction; Applications and Software; Technological Innovation; Marketing; Segmentation; AI and Machine Learning; Construction Industry; Turkey
Mojir, Navid, and Gamze Yucaoglu. "Borusan CAT: Monetizing Prediction in the Age of AI (A)." Harvard Business School Case 521-053, April 2021. (Revised August 2021.)
- 2002
- Other Unpublished Work
The Maturity of Debt Issues and Predictable Variation in Bond Returns
By: Malcolm Baker, Robin Greenwood and Jeffrey Wurgler
The maturity of new debt issues predicts excess bond returns. When the share of long term debt issues in total debt issues is high, future excess bond returns are low. This predictive power comes in two parts. First, inflation, the real short-term rate, and the term... View Details
Keywords: Borrowing and Debt; Bonds; Investment Return; Financial Markets; Forecasting and Prediction
Baker, Malcolm, Robin Greenwood, and Jeffrey Wurgler. "The Maturity of Debt Issues and Predictable Variation in Bond Returns." 2002. (First draft in 2001.)
- April 2012
- Article
The Predictive Value of Accruals and Consequences for Market Anomalies
By: Francois Brochet, Seunghan Nam and Joshua Ronen
We revisit the role of the cash and accrual components of accounting earnings in predicting future cash flows using out-of-sample predictions and market value of equity as a proxy for all future cash flows. We find that, on average, accruals improve upon current cash... View Details
Keywords: Forecasting and Prediction; Investment Return; Value; Equity; Markets; Cash Flow; Information Management; Accrual Accounting; Earnings Management; Corporate Governance; Stocks
Brochet, Francois, Seunghan Nam, and Joshua Ronen. "The Predictive Value of Accruals and Consequences for Market Anomalies." Journal of Accounting, Auditing & Finance 27, no. 2 (April 2012).
- Article
Why Grit Requires Perseverance and Passion to Positively Predict Performance
By: Jon M. Jachimowicz, Andreas Wihler, Erica R. Bailey and Adam D. Galinsky
Prior studies linking grit—defined as perseverance and passion for long-term goals—to performance are beset by contradictory evidence. As a result, commentators have increasingly declared that grit has limited effects. We propose that this inconsistent evidence has... View Details
Keywords: Grit; Perseverance; Passion; Motivation; Personal Characteristics; Emotions; Performance; Motivation and Incentives
Jachimowicz, Jon M., Andreas Wihler, Erica R. Bailey, and Adam D. Galinsky. "Why Grit Requires Perseverance and Passion to Positively Predict Performance." Proceedings of the National Academy of Sciences 115, no. 40 (October 2, 2018): 9980–9985.
- January 2010
- Journal Article
A Choice Prediction Competition: Choices from Experience and from Description
By: Ido Erev, Eyal Ert, Alvin E. Roth, Ernan E. Haruvy, Stefan Herzog, Robin Hau, Ralph Hertwig, Terrence Steward, Robert West and Christian Lebiere
Erev, Ert, and Roth organized three choice prediction competitions focused on three related choice tasks: one-shot decisions from description (decisions under risk), one-shot decisions from experience, and repeated decisions from experience. Each competition was based... View Details
Keywords: Experience and Expertise; Decision Choices and Conditions; Forecasting and Prediction; Mathematical Methods; Risk and Uncertainty; Competition
Erev, Ido, Eyal Ert, Alvin E. Roth, Ernan E. Haruvy, Stefan Herzog, Robin Hau, Ralph Hertwig, Terrence Steward, Robert West, and Christian Lebiere. "A Choice Prediction Competition: Choices from Experience and from Description." Special Issue on Decisions from Experience. Journal of Behavioral Decision Making 23, no. 1 (January 2010).
- 2023
- Article
Provable Detection of Propagating Sampling Bias in Prediction Models
By: Pavan Ravishankar, Qingyu Mo, Edward McFowland III and Daniel B. Neill
With an increased focus on incorporating fairness in machine learning models, it becomes imperative not only to assess and mitigate bias at each stage of the machine learning pipeline but also to understand the downstream impacts of bias across stages. Here we consider... View Details
Ravishankar, Pavan, Qingyu Mo, Edward McFowland III, and Daniel B. Neill. "Provable Detection of Propagating Sampling Bias in Prediction Models." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 8 (2023): 9562–9569. (Presented at the 37th AAAI Conference on Artificial Intelligence (2/7/23-2/14/23) in Washington, DC.)
- 9 Dec 2016
- Conference Presentation
Discovering Unknown Unknowns of Predictive Models
By: Himabindu Lakkaraju, Ece Kamar, Rich Caruana and Eric Horvitz
Lakkaraju, Himabindu, Ece Kamar, Rich Caruana, and Eric Horvitz. "Discovering Unknown Unknowns of Predictive Models." Paper presented at the 30th Annual Conference on Neural Information Processing Systems (NIPS), Workshop on Reliable Machine Learning in the Wild, Barcelona, Spain, December 9, 2016.
- Article
Liquidity Provision and Stock Return Predictability
By: Mark Seasholes and Terrence Hendershott
This paper examines the trading behavior of two groups of liquidity providers (specialists and competing market makers) using a six-year panel of NYSE data. Trades of each group are negatively correlated with contemporaneous price changes. To test for return... View Details
Keywords: Liquidity; Market Makers; Market Efficiency; Inventory; Liquidity Provision; Market Design; Financial Liquidity; Stocks; Investment Return
Seasholes, Mark, and Terrence Hendershott. "Liquidity Provision and Stock Return Predictability." Journal of Banking & Finance 45 (August 2014): 140–151.
- 2022
- Working Paper
Machine Learning Models for Prediction of Scope 3 Carbon Emissions
By: George Serafeim and Gladys Vélez Caicedo
For most organizations, the vast amount of carbon emissions occur in their supply chain and in the post-sale processing, usage, and end of life treatment of a product, collectively labelled scope 3 emissions. In this paper, we train machine learning algorithms on 15... View Details
Keywords: Carbon Emissions; Climate Change; Environment; Carbon Accounting; Machine Learning; Artificial Intelligence; Digital; Data Science; Environmental Sustainability; Environmental Management; Environmental Accounting
Serafeim, George, and Gladys Vélez Caicedo. "Machine Learning Models for Prediction of Scope 3 Carbon Emissions." Harvard Business School Working Paper, No. 22-080, June 2022.
- March 2018
- Article
Polluted Morality: Air Pollution Predicts Criminal Activity and Unethical Behavior
By: Jackson G. Lu, Julia J. Lee, F. Gino and Adam D. Galinsky
Air pollution is a serious problem that influences billions of people globally. Although the health and environmental costs of air pollution are well known, the present research investigates its ethical costs. We propose that air pollution can increase criminal and... View Details
Lu, Jackson G., Julia J. Lee, F. Gino, and Adam D. Galinsky. "Polluted Morality: Air Pollution Predicts Criminal Activity and Unethical Behavior." Psychological Science 29, no. 3 (March 2018): 340–355.